Deep Learning

Catalog description

Course Description: Deep learning theory, models, and applications. Mathematical foundations, optimization, multi-layer perceptrons, autoencoders, convolutional neural networks, recurrent neural networks (LSTM, GRU), generative adversarial networks, word embeddings, transformers, BERT, and graph neural networks. Hands-on experience implementing deep learning models using PyTorch for image classification, text classification and generation, and graph node classification.